Repeated Human Exposure to Semivolatile Organic Compounds by Inhalation: Novel Protocol for a Nonrandomized Study
Bibliographic record
Abstract
BACKGROUND: Semivolatile organic compounds (SVOCs) comprise several different chemical families used mainly as additives in many everyday products. SVOCs can be released into the air as aerosols and deposit on particulate matter during use by dispersion, evaporation, or abrasion. Phthalates are SVOCs of growing concern due to their endocrine-disrupting effects. Human data on the absorption, distribution, metabolism, and excretion (ADME) of these compounds upon inhalation are almost nonexistent. OBJECTIVE: The goal of this study is to develop a method for repeated inhalation exposures to SVOCs to characterize their ADME in humans. METHODS: We will use diethylhexyl phthalate (DEHP), a major indoor air pollutant, as a model SVOC in this novel protocol. The Swiss official Commission on Ethics in Human Research, Canton de Vaud, approved the study on October 14, 2020 (project-ID 2020-01095). Participants (n=10) will be repeatedly exposed (2 short daily exposures over 4 days) to isotope-labeled DEHP (DEHP-d4) to distinguish administered exposures from background exposures. DEHP-d4 aerosols will be generated with a small, portable, aerosol-generating device. Participants will inhale DEHP-d4-containing aerosols themselves with this device at home. Air concentrations of the airborne phthalates will be less than or equal to their occupational exposure limit (OEL). DEHP-d4 and its metabolites will be quantified in urine and blood before, during, and after exposure. RESULTS: Our developed device can generate DEHP-d4 aerosols with diameters of 2.5 μm or smaller and a mean DEHP-d4 mass of 1.4 (SD 0.2) μg per puff (n=6). As of May 2023, we have enrolled 5 participants. CONCLUSIONS: The portable device can be used to generate phthalate aerosols for repeated exposure in human studies. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/51020.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".